Recently, the RNA-seq technology is increasingly replacing microarray for expression profiling. In this proposal, we will timely address the emerging challenges and opportunities brought by the rapidly accumulating RNA-seq data. We will design novel methods to perform integrative analysis of many RNA-seq datasets to study the functions and regulations of alternative splicing. In particular, we have the following specifi aims: (1) We will develop a novel graph- based pattern mining method to reconstruct an atlas of splicing modules and identify the associated experimental conditions in human, mouse, fly, and yeast. (2) We will study the coupling between transcription and splicing, the two important regulatory processes, by exploiting both expression and splicing information provided by RNA-seq data. We will design a novel multi-layer network mining approach to systematically identify coupled transcription- splicing modules. (3) We will predict the functions of alternatively spliced transcripts to establish a high-resolution function annotation of human genome. The predicted functions will be incorporated into the GeneOntology and the Ensembl databases to benefit the biological community. (4) We will perform experimental validation on a subset of computational predictions made in Aims 1, 2, 3. (5) We will develop web databases and software to directly benefit the scientific community. Our methods and software will significantly facilitate the re-use of the vast amount of existing RNA-seq data, reduce the necessity to generate new data, and improve our understanding of gene regulations under a variety of perturbations.